Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

tissue_of_interest = "Diaphragm"

Load the requisite packages and some additional helper functions.

library(here)
here() starts at /Users/olgabot/code/tabula-muris
library(useful)
Loading required package: ggplot2
library(Seurat)
Loading required package: cowplot

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave
Loading required package: Matrix
Warning: namespace 'Biobase' is not available and has been replaced
by .GlobalEnv when processing object 'call.'
Warning: namespace 'lme4' is not available and has been replaced
by .GlobalEnv when processing object 'call.'
Warning: namespace 'MatrixModels' is not available and has been replaced
by .GlobalEnv when processing object 'call.'
Warning: namespace 'Biobase' is not available and has been replaced
by .GlobalEnv when processing object 'call.'
Warning: namespace 'lme4' is not available and has been replaced
by .GlobalEnv when processing object 'call.'
Warning: namespace 'MatrixModels' is not available and has been replaced
by .GlobalEnv when processing object 'call.'
library(dplyr)
Warning: package 'dplyr' was built under R version 3.4.2

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(Matrix)

save_dir = here('00_data_ingest', 'tissue_robj')

Load the plate metadata. Check which plates have been downloaded.

plate_metadata_filename = here('00_data_ingest', '00_facs_raw_data', 'metadata_FACS.csv')

plate_metadata <- read.csv(plate_metadata_filename, sep=",", header = TRUE)
colnames(plate_metadata)[1] <- "plate.barcode"
plate_metadata

“Diaphragm” is a subtissue of “Muscle” and we will have to filter for this.

tissue_plates = filter(plate_metadata, subtissue == tissue_of_interest)[,c('plate.barcode','tissue','subtissue','mouse.sex')]
tissue_plates

Load the read count data.

#Load the gene names and set the metadata columns by opening the first file
filename = here('00_data_ingest', '00_facs_raw_data', 'FACS', 'Muscle-counts.csv')

raw.data = read.csv(filename, sep=",", row.names=1)
# raw.data = data.frame(row.names = rownames(raw.data))
corner(raw.data)

Get the plate barcode of each individual cell

plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2])
meta.data = plate_metadata[plate_metadata$plate.barcode %in% plate.barcodes, ]
dim(plate_metadata)
[1] 247   6
dim(meta.data)
[1] 10  6
corner(meta.data)

Use only cells from “Diaphragm”" plate barcodes.

subtissue.plates = filter(tissue_plates, subtissue == tissue_of_interest)
subtissue.plates = plate.barcodes %in% subtissue.plates$plate.barcode
sum(subtissue.plates)
[1] 951
raw.data = raw.data[subtissue.plates]

Create per-cell metadata from plate barcode metadata

barcode.df = t.data.frame(as.data.frame(plate.barcodes[subtissue.plates]))
head(barcode.df)
             [,1]     
X.D042105.   "D042105"
X.D042105..1 "D042105"
X.D042105..2 "D042105"
X.D042105..3 "D042105"
X.D042105..4 "D042105"
X.D042105..5 "D042105"
D042105

D042105

D042105

D042105

D042105

D042105
rownames(barcode.df) = colnames(raw.data)
colnames(barcode.df) = c('plate.barcode')
head(barcode.df)
                       plate.barcode
A8.D042105.3_11_M.1.1  "D042105"    
K10.D042105.3_11_M.1.1 "D042105"    
L13.D042105.3_11_M.1.1 "D042105"    
M15.D042105.3_11_M.1.1 "D042105"    
N17.D042105.3_11_M.1.1 "D042105"    
O19.D042105.3_11_M.1.1 "D042105"    
D042105

D042105

D042105

D042105

D042105

D042105
rnames = row.names(barcode.df)
meta.data <- merge(barcode.df, plate_metadata, by='plate.barcode', sort = F)
row.names(meta.data) <- rnames

# Sort cells by plate barcode because that's how the data was originally
meta.data = meta.data[order(meta.data$plate.barcode), ]
raw.data = raw.data[, rownames(meta.data)]
head(raw.data)
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]

tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest, 
                    min.cells = 5, min.genes = 5)

tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'

Calculate percent ribosomal genes.

ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)

percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)

tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")

A sanity check: reads vs genes.

GenePlot(object = tiss, gene1 = "nReads", gene2 = "nGene")

GenePlot(object = tiss, gene1 = "Pax7", gene2 = "Pax3")

GenePlot(object = tiss, gene1 = "Pax7", gene2 = "Myod1")

Filter out cells with few reads and few genes.

tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nReads"), 
    low.thresholds = c(500, 50000), high.thresholds = c(25000, 2000000))

Normalize the data, then regress out correlation with total reads

tiss <- NormalizeData(object = tiss)
tiss <- ScaleData(object = tiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))
[1] "Regressing out nReads"       "Regressing out percent.ribo"
[3] "Regressing out Rn45s"       

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[1] "Scaling data matrix"

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tiss.back <- tiss
#tiss <- tiss.back
tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)

Run Principal Component Analysis.

tiss <- RunPCA(object = tiss, pc.genes = tiss@var.genes, do.print = TRUE, pcs.print = 1:5, 
    genes.print = 5)
[1] "PC1"
[1] "Gsn"    "Errfi1" "Des"    "Chodl"  "Musk"  
[1] ""
[1] "Srgn"    "Arhgdib" "Coro1a"  "Cd74"    "H2-Ab1" 
[1] ""
[1] ""
[1] "PC2"
[1] "Ctsh"     "AI607873" "Ctss"     "Ecm1"     "Lyz2"    
[1] ""
[1] "Flt1"    "Fabp4"   "Gpihbp1" "Apold1"  "Ptprb"  
[1] ""
[1] ""
[1] "PC3"
[1] "C1qc" "C1qa" "C1qb" "Lyz2" "Ccl6"
[1] ""
[1] "Mfap5"  "Lum"    "Dcn"    "Col1a2" "Ly6a"  
[1] ""
[1] ""
[1] "PC4"
[1] "Cd79a"   "Cd79b"   "H2-DMb2" "Ms4a1"   "Ly6d"   
[1] ""
[1] "C1qc"     "C1qa"     "Ecm1"     "AI607873" "C1qb"    
[1] ""
[1] ""
[1] "PC5"
[1] "Cd79a"   "Ms4a1"   "H2-DMb2" "Ly6d"    "Cd79b"  
[1] ""
[1] "Il7r"  "Cd3g"  "Skap1" "Itk"   "Emb"  
[1] ""
[1] ""
tiss <- ProjectPCA(object = tiss, do.print = FALSE)

PCElbowPlot(object = tiss)

n.pcs = 10
#tiss <- tiss.back
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = 1, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, check_duplicates = F, perplexity=30)
pt.size = 0.5
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T, pt.size = pt.size)

Check expression of genes of interest.

genes_to_check = c('Pecam1', 'Ptprc', 'Vcam1', 'Pdgfra')

#FeaturePlot(tiss, genes_to_check, pt.size = 0.5, cols.use=c("yellow", "black"), no.legend = FALSE)
FeaturePlot(tiss, genes_to_check, pt.size = pt.size, no.legend = FALSE)

# SC - subtypes
genes_to_check = c('Vcam1', 'Pax7', 'Pax3', 'Myod1', 'Myf5', 'Cd34', 'Itga7', 'Cd44', 'Calcr' #, 'Myog'
                   )
FeaturePlot(tiss, genes_to_check, pt.size = pt.size)

# CD45 - macrophage subtypes
genes_to_check = c('Ptprc', 
                   'Klrb1b', 'Foxp3', # NK-cells
                   'Cd3d', #T-cell
                   'Cd19', 'Cd79a', 'Cd79b', #differentiated B cells
                   'Vpreb3', #naive B-cells
                   'Itgam', 'Fcer1g', 'C1qa' #macrophages and mast cells
                   #, 'Ly6g6d', 
)
FeaturePlot(tiss, genes_to_check, pt.size = pt.size)

# CD31 - endothelial subtypes
genes_to_check = c('Pecam1' #, 'Vegfa', 'Cd34', 'Ptprc', 'Icam1' #, 'Dysf' #'Rapgef3', 'Vim', 'Cspg4'
)
FeaturePlot(tiss, genes_to_check, pt.size = pt.size)

# FAP -  subtypes
genes_to_check = c('Atxn1', 'Pdgfra' #'Dysf', 'Vim', 'Cspg4'
)
FeaturePlot(tiss, genes_to_check, pt.size = pt.size)

How big are the clusters?

table(tiss@ident)

  0   1   2   3   4   5 
240 228 214  80  77  31 

Color by metadata, like plate barcode, to check for batch effects.

#TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")
TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.id", pt.size = pt.size)

#table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
table(as.character(tiss@ident), as.character(tiss@meta.data$mouse.id))
   
    3_10_M 3_11_M 3_38_F 3_39_F
  0     45     70     59     66
  1     49    164      1     14
  2      4      7     72    131
  3     21     23     15     21
  4      5     40      3     29
  5      4     15      7      5

Find differentially expressed markers.

tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, 
    thresh.use = 0.25)

Display top 24 markers per cluster.

tiss.markers %>% group_by(cluster) %>% top_n(24, avg_diff)
# To change the y-axis to show raw counts, add use.raw = T.
genes_to_check = c('Pecam1', 'Ptprc', 'Vcam1', 'Pdgfra', 'Cd19')
VlnPlot(tiss, genes_to_check #, pt.size = pt.size
        )

Assigning cell type identity to clusters At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types: 0: alpha 1: beta 2: beta 3: exocrine 4: duct 5: delta 6: gamma 7: endothelial 8: immune 9: stellate

# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")

# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5)
annotation <- c("mesenchymal stem cell", "skeletal muscle satellite stem cell", "skeletal muscle satellite stem cell", "B cell", "endothelial cell", "macrophage")
cell_ontology_id <- c("CL:0000134", "CL:0008011", "CL:0008011", "CL:0000236", "CL:0000115", "CL:0000235")

tiss@meta.data[,'annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = annotation)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)

tiss@meta.data[tiss@cell.names,'annotation'] <- as.character(tiss@meta.data$annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)


TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='annotation')

tiss = BuildClusterTree(tiss)
[1] "Finished averaging RNA for cluster 0"
[1] "Finished averaging RNA for cluster 1"
[1] "Finished averaging RNA for cluster 2"
[1] "Finished averaging RNA for cluster 3"
[1] "Finished averaging RNA for cluster 4"
[1] "Finished averaging RNA for cluster 5"

TODO run after this

# Get markers for a particular cluster
cluster_markers = filter(tiss.markers, cluster == 3)$gene

DotPlot(tiss, genes.plot = cluster_markers[1:12], plot.legend = T)

We can repeat the above analysis on a subset of genes, defined using cluster IDs or expression or some other metadata. This is a good way to drill down and find substructure.

# To subset data based on annotation or other metadata, you can explicitly pass cell names
anno = 'B cell'
cells.to.use = tiss@cell.names[which(tiss@meta.data$annotation == anno)]
subtiss <- SubsetData(object = tiss, cells.use = cells.to.use, do.center = F, do.scale = F)

subtiss <- NormalizeData(object = subtiss)
subtiss <- ScaleData(object = subtiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))
[1] "Regressing out nReads"       "Regressing out percent.ribo"
[3] "Regressing out Rn45s"       

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[1] "Scaling data matrix"

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#subtiss@scale.data = subtiss@data
#subtiss <- ScaleData(object = subtiss)
subtiss <- FindVariableGenes(object = subtiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)

subtiss <- RunPCA(object = subtiss, pcs.compute = 20)
[1] "PC1"
 [1] "H2-Aa"         "H2-Ab1"        "Cd74"          "Cd79a"        
 [5] "H2-Eb1"        "H2-DMb2"       "Ly6d"          "Cd79b"        
 [9] "H2-DMa"        "Napsa"         "Faim3"         "Cd83"         
[13] "2010001M09Rik" "Unc93b1"       "Plac8"         "Ly6a"         
[17] "Mef2c"         "Ctsz"          "H2-Oa"         "Syngr2"       
[21] "Cd19"          "Ifi30"         "Plaur"         "Scd1"         
[25] "Arhgdib"       "Capg"          "Fth1"          "Tmsb4x"       
[29] "Ucp2"          "Cd81"         
[1] ""
 [1] "Il7r"     "Il2rb"    "Cd3g"     "Cd247"    "Ppp1r14c" "Cxcr6"   
 [7] "Smox"     "Adam12"   "Emb"      "Atp2b4"   "Icos"     "Il1r1"   
[13] "Cd3e"     "Lxn"      "Zap70"    "Dgat1"    "Itk"      "Cd163l1" 
[19] "Tmem66"   "Kcnk1"    "Ramp1"    "Rora"     "Gcnt2"    "Lancl2"  
[25] "Tmem64"   "Itgb4"    "Ppp3r1"   "Spry2"    "Igf1r"    "Tnfrsf9" 
[1] ""
[1] ""
[1] "PC2"
 [1] "Junb"          "Ppp1r14c"      "Il1r1"         "Adam12"       
 [5] "9430091E24Rik" "Lxn"           "Sdc1"          "Tpra1"        
 [9] "Itgb4"         "Bag3"          "Apaf1"         "Gpnmb"        
[13] "Des"           "Tacstd2"       "Gng4"          "Zw10"         
[17] "Atp2b4"        "Pfkfb2"        "Igf1r"         "Zmpste24"     
[21] "2310001A20Rik" "Kcnk1"         "Ppfibp1"       "Lancl2"       
[25] "Slc35e2"       "Zfp692"        "Tmem176b"      "Kbtbd4"       
[29] "Zfp64"         "Gcnt2"        
[1] ""
 [1] "Tpx2"   "Ube2c"  "Ddah2"  "Rrm2"   "Kif11"  "Gpr3"   "Fam83d"
 [8] "Hmmr"   "Cenpf"  "Dntt"   "Kifc1"  "Top2a"  "Cenpe"  "Dos"   
[15] "Tacc3"  "Ccnb2"  "Cdk1"   "Fbxo5"  "Ly6k"   "Ccdc92" "Cdc20" 
[22] "Mepce"  "Mogs"   "Cd8a"   "Rnf26"  "Rnf123" "Socs4"  "Srsf1" 
[29] "Cdca3"  "Nmt2"  
[1] ""
[1] ""
[1] "PC3"
 [1] "Apaf1"         "Des"           "Gng4"          "Zw10"         
 [5] "Gpnmb"         "Tacstd2"       "Sdc1"          "2310001A20Rik"
 [9] "Itgb4"         "Tpra1"         "9430091E24Rik" "Akap12"       
[13] "Ccng2"         "Kbtbd4"        "Pfkfb2"        "Ppfibp1"      
[17] "Dnajc12"       "Lyrm5"         "Bag3"          "Rab3gap2"     
[21] "Dtwd2"         "Ppp3r1"        "Pdpk1"         "Mesdc2"       
[25] "Hdc"           "Slc35e2"       "Muc1"          "2610204G22Rik"
[29] "Sorbs1"        "Acp6"         
[1] ""
 [1] "Il2ra"         "Dennd4c"       "Srxn1"         "Tnfsf11"      
 [5] "Ltb4r1"        "Enpp5"         "Bace2"         "Inpp1"        
 [9] "Spata5"        "D030028A08Rik" "Plxdc2"        "Fbxl8"        
[13] "Socs2"         "Ramp3"         "Tnfrsf1a"      "Zdhhc9"       
[17] "Rgs1"          "Lrwd1"         "Acadvl"        "Flot2"        
[21] "Rnf166"        "Dpp7"          "Nedd4"         "Dusp5"        
[25] "Dhcr24"        "Rcbtb2"        "Rasgrp1"       "Scap"         
[29] "Zfp689"        "9930104L06Rik"
[1] ""
[1] ""
[1] "PC4"
 [1] "Fbxw5"         "Hsdl1"         "Tasp1"         "D230025D16Rik"
 [5] "Hipk2"         "BC025920"      "Cyp39a1"       "Aldh1b1"      
 [9] "Adam22"        "Ccdc93"        "Prkch"         "2610019F03Rik"
[13] "Btg1"          "Ly6c2"         "Gbp9"          "Pira2"        
[17] "Nkg7"          "Ccr7"          "Nfrkb"         "Sesn3"        
[21] "Ncoa5"         "Cd27"          "G6pdx"         "Smcr7l"       
[25] "Rassf5"        "AI837181"      "Dirc2"         "Ets1"         
[29] "Osbpl2"        "Tigit"        
[1] ""
 [1] "S100a6"    "Tagln2"    "Capg"      "Lmna"      "Dnase1l3" 
 [6] "Actg1"     "Pnpo"      "Ccnd2"     "Tppp3"     "Ckb"      
[11] "Hmga2-ps1" "Ifi30"     "Ccdc109a"  "Plac8"     "Trak2"    
[16] "Sgta"      "S100a4"    "Lsp1"      "Ltb4r1"    "Hk2"      
[21] "Tubb6"     "Myg1"      "Crip1"     "Nsdhl"     "Cd9"      
[26] "Cyb5r3"    "Fgl2"      "Il2ra"     "Igf1r"     "Fam86"    
[1] ""
[1] ""
[1] "PC5"
 [1] "Ccdc97"   "Krr1"     "Sub1"     "Spop"     "Snrnp200" "Xylt1"   
 [7] "Pofut2"   "Dos"      "Pira2"    "Snip1"    "Zc3hav1"  "Ano6"    
[13] "Clcn4-2"  "Taf4b"    "Clasp1"   "Cmtm8"    "Snx27"    "Dalrd3"  
[19] "Zfp167"   "Mepce"    "Ttc3"     "Rassf5"   "Slc37a2"  "Vcpip1"  
[25] "Slc38a10" "Ubr3"     "Mcoln2"   "Apoo"     "Ncoa5"    "AI837181"
[1] ""
 [1] "Cdc6"          "Tox"           "Asf1b"         "Aurkb"        
 [5] "Dpp8"          "Tmem48"        "Hdac8"         "Atpaf2"       
 [9] "Txnrd1"        "Zdhhc16"       "Nup88"         "Casp8"        
[13] "Cenpm"         "Socs5"         "Pxmp3"         "Parvg"        
[17] "Strada"        "Rnf146"        "Prpf3"         "2810417H13Rik"
[21] "Hdac10"        "Mtap"          "Dpcd"          "Cox10"        
[25] "Creg1"         "Eed"           "Brf1"          "Pold1"        
[29] "Gins2"         "Rnf41"        
[1] ""
[1] ""
print("de2")
[1] "de2"
subtiss <- ProjectPCA(object = subtiss, do.print = FALSE)
PCElbowPlot(object = subtiss)

PCHeatmap(object = subtiss, pc.use = 1:9, cells.use = 80, do.balanced = TRUE, label.columns = FALSE, num.genes = 18)

subtiss <- FindClusters(object = subtiss, reduction.type = "pca", dims.use = 1:5, 
    resolution = 0.5, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
subtiss <- RunTSNE(object = subtiss, dims.use = 1:10, seed.use = 10, check_duplicates = F, perplexity=25)
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = subtiss, do.label = T)

Check expression of genes of interset.

genes_to_check = c('Pax7', 'Pax3', 'Myod1', 'Myf5')

FeaturePlot(subtiss, genes_to_check, pt.size = 1)
Warning in (function (data.use, feature, data.plot, pt.size, pch.use,
cols.use, : All cells have the same value of Pax7.
Warning in (function (data.use, feature, data.plot, pt.size, pch.use,
cols.use, : All cells have the same value of Pax3.
Warning in (function (data.use, feature, data.plot, pt.size, pch.use,
cols.use, : All cells have the same value of Myf5.

GenePlot(subtiss, 'Pax7', 'Pax3', use.raw = T)
Warning in cor(x = data.plot$x, y = data.plot$y): the standard deviation is
zero

When you save the annotated tissue, please give it a name.

filename = here('00_data_ingest', '04_tissue_robj_generated', 
                     paste0(tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
[1] "/Users/olgabot/code/tabula-muris/00_data_ingest/04_tissue_robj_generated/Diaphragm_seurat_tiss.Robj"
save(tiss, file=filename)

Export the final metadata

So that Biohub can easily combine all your annotations, please export them as a simple csv.

head(tiss@meta.data)
filename = here('00_data_ingest', '03_tissue_annotation_csv', 
                     paste0(tissue_of_interest, "_annotation.csv"))
write.csv(tiss@meta.data[,c('plate.barcode','annotation','cell_ontology_id')], file=filename)
---
title: "Diaphragm FACS Notebook"
output: html_notebook
---

Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

```{r}
tissue_of_interest = "Diaphragm"
```

Load the requisite packages and some additional helper functions.

```{r}
library(here)
library(useful)
library(Seurat)
library(dplyr)
library(Matrix)

save_dir = here('00_data_ingest', 'tissue_robj')
```

Load the plate metadata. Check which plates have been downloaded.

```{r}
plate_metadata_filename = here('00_data_ingest', 'facs_raw_data', 'metadata_FACS.csv')

plate_metadata <- read.csv(plate_metadata_filename, sep=",", header = TRUE)
colnames(plate_metadata)[1] <- "plate.barcode"
plate_metadata
```

"Diaphragm" is a subtissue of "Muscle" and we will have to filter for this.

```{r}
tissue_plates = filter(plate_metadata, subtissue == tissue_of_interest)[,c('plate.barcode','tissue','subtissue','mouse.sex')]
tissue_plates
```
Load the read count data. 
```{r}
#Load the gene names and set the metadata columns by opening the first file
filename = here('00_data_ingest', 'facs_raw_data', 'FACS', 'Muscle-counts.csv')

raw.data = read.csv(filename, sep=",", row.names=1)
# raw.data = data.frame(row.names = rownames(raw.data))
corner(raw.data)
```

Get the plate barcode of each individual cell

```{r}
plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2])
meta.data = plate_metadata[plate_metadata$plate.barcode %in% plate.barcodes, ]
dim(plate_metadata)
dim(meta.data)
corner(meta.data)
```
Use only cells from "Diaphragm"" plate barcodes.

```{r}
subtissue.plates = filter(tissue_plates, subtissue == tissue_of_interest)
subtissue.plates = plate.barcodes %in% subtissue.plates$plate.barcode
sum(subtissue.plates)

raw.data = raw.data[subtissue.plates]
```

Create per-cell metadata from plate barcode metadata

```{r}
barcode.df = t.data.frame(as.data.frame(plate.barcodes[subtissue.plates]))
head(barcode.df)

rownames(barcode.df) = colnames(raw.data)
colnames(barcode.df) = c('plate.barcode')
head(barcode.df)

rnames = row.names(barcode.df)
meta.data <- merge(barcode.df, plate_metadata, by='plate.barcode', sort = F)
row.names(meta.data) <- rnames

# Sort cells by plate barcode because that's how the data was originally
meta.data = meta.data[order(meta.data$plate.barcode), ]
raw.data = raw.data[, rownames(meta.data)]
head(raw.data)
```


```{r}

erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]

tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest, 
                    min.cells = 5, min.genes = 5)

tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'
```


Calculate percent ribosomal genes.

```{r}
ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)

percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)

tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")
```

A sanity check: reads vs genes.

```{r}
GenePlot(object = tiss, gene1 = "nReads", gene2 = "nGene")
GenePlot(object = tiss, gene1 = "Pax7", gene2 = "Pax3")
GenePlot(object = tiss, gene1 = "Pax7", gene2 = "Myod1")
```

Filter out cells with few reads and few genes.

```{r}
tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nReads"), 
    low.thresholds = c(500, 50000), high.thresholds = c(25000, 2000000))
```


Normalize the data, then regress out correlation with total reads
```{r}
tiss <- NormalizeData(object = tiss)
tiss <- ScaleData(object = tiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))
tiss.back <- tiss
```

```{r}
#tiss <- tiss.back
tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)
```


Run Principal Component Analysis.
```{r}
tiss <- RunPCA(object = tiss, pc.genes = tiss@var.genes, do.print = TRUE, pcs.print = 1:5, 
    genes.print = 5)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)
```


```{r, echo=FALSE, fig.height=6, fig.width=8}
PCHeatmap(object = tiss, pc.use = 1:9, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 16)
tiss.back <- tiss
```

```{r}
PCElbowPlot(object = tiss)
```


```{r}
n.pcs = 10
#tiss <- tiss.back
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = 1, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
```


```{r}
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, check_duplicates = F, perplexity=30)
```

```{r}
pt.size = 0.5
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T, pt.size = pt.size)
```

Check expression of genes of interest.
```{r, fig.height=6, fig.width=8}
genes_to_check = c('Pecam1', 'Ptprc', 'Vcam1', 'Pdgfra')

#FeaturePlot(tiss, genes_to_check, pt.size = 0.5, cols.use=c("yellow", "black"), no.legend = FALSE)
FeaturePlot(tiss, genes_to_check, pt.size = pt.size, no.legend = FALSE)
```


```{r, fig.height=6, fig.width=8}
# SC - subtypes
genes_to_check = c('Vcam1', 'Pax7', 'Pax3', 'Myod1', 'Myf5', 'Cd34', 'Itga7', 'Cd44', 'Calcr' #, 'Myog'
                   )
FeaturePlot(tiss, genes_to_check, pt.size = pt.size)
```

```{r, fig.height=6, fig.width=8}
# CD45 - macrophage subtypes
genes_to_check = c('Ptprc', 
                   'Klrb1b', 'Foxp3', # NK-cells
                   'Cd3d', #T-cell
                   'Cd19', 'Cd79a', 'Cd79b', #differentiated B cells
                   'Vpreb3', #naive B-cells
                   'Itgam', 'Fcer1g', 'C1qa' #macrophages and mast cells
                   #, 'Ly6g6d', 
)
FeaturePlot(tiss, genes_to_check, pt.size = pt.size)
```


```{r}
# CD31 - endothelial subtypes
genes_to_check = c('Pecam1' #, 'Vegfa', 'Cd34', 'Ptprc', 'Icam1' #, 'Dysf' #'Rapgef3', 'Vim', 'Cspg4'
)
FeaturePlot(tiss, genes_to_check, pt.size = pt.size)
```

```{r}
# FAP -  subtypes
genes_to_check = c('Atxn1', 'Pdgfra' #'Dysf', 'Vim', 'Cspg4'
)
FeaturePlot(tiss, genes_to_check, pt.size = pt.size)
```
How big are the clusters?
```{r}
table(tiss@ident)
```

Color by metadata, like plate barcode, to check for batch effects.
```{r}
#TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")
TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.id", pt.size = pt.size)
#table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
table(as.character(tiss@ident), as.character(tiss@meta.data$mouse.id))
```

Find differentially expressed markers.
```{r}
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, 
    thresh.use = 0.25)
```

Display top 24 markers per cluster.
```{r}
tiss.markers %>% group_by(cluster) %>% top_n(24, avg_diff)
```


```{r}
# To change the y-axis to show raw counts, add use.raw = T.
genes_to_check = c('Pecam1', 'Ptprc', 'Vcam1', 'Pdgfra', 'Cd19')
VlnPlot(tiss, genes_to_check #, pt.size = pt.size
        )
```
Assigning cell type identity to clusters
At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:
0: alpha 1: beta 2: beta 3: exocrine 4: duct 5: delta 6: gamma 7: endothelial 8: immune 9: stellate
```{r}
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")

# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5)
annotation <- c("mesenchymal stem cell", "skeletal muscle satellite stem cell", "skeletal muscle satellite stem cell", "B cell", "endothelial cell", "macrophage")
cell_ontology_id <- c("CL:0000134", "CL:0008011", "CL:0008011", "CL:0000236", "CL:0000115", "CL:0000235")

tiss@meta.data[,'annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = annotation)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)

tiss@meta.data[tiss@cell.names,'annotation'] <- as.character(tiss@meta.data$annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)


TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='annotation')
```


```{r}
tiss = BuildClusterTree(tiss)
```

## TODO run after this

```{r}
# Get markers for a particular cluster
cluster_markers = filter(tiss.markers, cluster == 3)$gene

DotPlot(tiss, genes.plot = cluster_markers[1:12], plot.legend = T)

```

We can repeat the above analysis on a subset of genes, defined using cluster IDs or expression or some other metadata. This is a good way to drill down and find substructure.

```{r}
# To subset data based on annotation or other metadata, you can explicitly pass cell names
anno = 'B cell'
cells.to.use = tiss@cell.names[which(tiss@meta.data$annotation == anno)]
subtiss <- SubsetData(object = tiss, cells.use = cells.to.use, do.center = F, do.scale = F)

subtiss <- NormalizeData(object = subtiss)
subtiss <- ScaleData(object = subtiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))

#subtiss@scale.data = subtiss@data
#subtiss <- ScaleData(object = subtiss)
```



```{r}
subtiss <- FindVariableGenes(object = subtiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)
subtiss <- RunPCA(object = subtiss, pcs.compute = 20)
print("de2")
subtiss <- ProjectPCA(object = subtiss, do.print = FALSE)
```


```{r}
PCElbowPlot(object = subtiss)
```

```{r, fig.height=6, fig.width=8}
PCHeatmap(object = subtiss, pc.use = 1:9, cells.use = 80, do.balanced = TRUE, label.columns = FALSE, num.genes = 18)
```


```{r}
subtiss <- FindClusters(object = subtiss, reduction.type = "pca", dims.use = 1:5, 
    resolution = 0.5, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
```


```{r}
subtiss <- RunTSNE(object = subtiss, dims.use = 1:10, seed.use = 10, check_duplicates = F, perplexity=25)
```

```{r}
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = subtiss, do.label = T)
```

Check expression of genes of interset.
```{r}
genes_to_check = c('Pax7', 'Pax3', 'Myod1', 'Myf5')

FeaturePlot(subtiss, genes_to_check, pt.size = 1)
```


```{r}
GenePlot(subtiss, 'Pax7', 'Pax3', use.raw = T)
```



When you save the annotated tissue, please give it a name.

```{r}
filename = here('00_data_ingest', 'tissue_seurat_robj', 
                     paste0(tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
```


# Export the final metadata

So that Biohub can easily combine all your annotations, please export them as a simple csv.

```{r}
head(tiss@meta.data)
```


```{r}
filename = here('00_data_ingest', 'tissue_annotation_csv', 
                     paste0(tissue_of_interest, "_annotation.csv"))
write.csv(tiss@meta.data[,c('plate.barcode','annotation','cell_ontology_id')], file=filename)
```

